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Application of Bayesian Statistical Methods for the Analysis of DSMC Simulations of Hypersonic Shocks James S. Strand and David B. Goldstein The University of Texas at Austin Sponsored by the Department of Energy through the PSAAP Program Predictive Engineering and Computational Sciences Computational Fluid Physics Laboratory

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Motivation – DSMC Parameters The DSMC model includes many parameters related to gas dynamics at the molecular level, such as: Elastic collision cross-sections. Vibrational and rotational excitation probabilities. Reaction cross-sections. Sticking coefficients and catalytic efficiencies for gas-surface interactions. …etc.

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DSMC Parameters In many cases the precise values of some of these parameters are not known. Parameter values often cannot be directly measured, instead they must be inferred from experimental results. By necessity, parameters must often be used in regimes far from where their values were determined. More precise values for important parameters would lead to better simulation of the physics, and thus to better predictive capability for the DSMC method. The ultimate goal of this work is to use experimental data to calibrate important DSMC parameters.

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DSMC Method Direct Simulation Monte Carlo (DSMC) is a particle based simulation method. Simulated particles represent large numbers of real particles. Particles move and undergo collisions with other particles. Can be used in highly non-equilibrium flowfields (such as strong shock waves). Can model thermochemistry on a more detailed level than most CFD codes.

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DSMC Method Initialize Move Index Collide Sample Performed on First Time Step Performed on Selected Time Steps Performed on Every Time Step Create

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Numerical Methods – DSMC Code Our DSMC code can model flows with rotational and vibrational excitation and relaxation, as well as five-species air chemistry, including dissociation, exchange, and recombination reactions. Larsen-Borgnakke model is used for redistribution between rotational, translational, and vibrational modes during inelastic collisions. TCE model provides cross-sections for chemical reactions.

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Chemistry Implementation σ R and σ E are the reaction and elastic cross-sections, respectively k is the Boltzmann constant, m r is the reduced mass of particles A and B, E c is the collision energy, and Γ() is the gamma function.

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Reactions R. Gupta, J. Yos, and R. Thompson, NASA Technical Memorandum 101528, 1989.

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1D Shock Simulation We require the ability to simulate a 1-D shock without knowing the post-shock conditions a priori. To this end, we simulate an unsteady 1-D shock, and make use of a sampling region which moves with the shock.

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Sensitivity Analysis - Overview In the current context, the goal of sensitivity analysis is to determine which parameters most strongly affect a given quantity of interest (QoI). Only parameters to which a given QoI is sensitive will be informed by calibrations based on data for that QoI. Sensitivity analysis is used here both to determine which parameters to calibrate in the future, and to select the QoI which would best inform the parameters we most wish to calibrate.

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Sensitivity Analysis Parameters Throughout the sensitivity analysis, the ratio of forward to backward rate for a given reaction is kept constant, since these ratios should be fixed by the equilibrium constant.

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Scenario: 1-D Shock Shock speed is ~8000 m/s, M ∞ ≈ 23. Upstream number density = 3.22×10 21 #/m 3. Upstream composition by volume: 79% N 2, 21% O 2. Upstream temperature = 300 K.

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Results: Nominal Parameter Values

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Quantity of Interest (QoI) J. Grinstead, M. Wilder, J. Olejniczak, D. Bogdanoff, G. Allen, and K. Danf, AIAA Paper 2008-1244, 2008. We cannot yet simulate EAST results, so we must choose a temporary, surrogate QoI. ?

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Sensitivity Analysis - Methods

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Sensitivity Analysis - Scalar vs. Vector QoI We use two QoI’s in this work, the bulk translational temperature and the density of NO. Each of these is a vector QoI, with values over a range of points in space.

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Sensitivity Analysis: Correlation Coefficient

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Sensitivity Analysis: Mutual Information

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Hypothetical joint PDF for case where the QoI is indepenent of θ 1. Actual joint PDF of θ 1 and the QoI, from a Monte Carlo sampling of the parameter space. QoI θ1θ1 Kullback-Leibler divergence

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Sensitivities vs. X for T trans,bulk as QoI

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Sensitivities vs. X for ρ NO as QoI

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r 2 vs. Mutual Information

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Sensitivities vs. X for ρ NO as QoI

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Variance of ρ NO vs. X

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Variance Weighted Sensitivities for ρ NO as QoI

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Overall Sensitivities

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Conclusions Global, Monte Carlo based sensitivity analysis can provide a great deal of insight into how various parameters affect a given QoI. Sensitivities based on r 2 are mostly similar to those based on the mutual information, but there are notable differences for some parameters. Which parameters have the highest sensitivities depends strongly on which QoI is chosen. If our goal is to calibrate as many parameters as possible, and we had available data, we would use ρ NO as our QoI, since it is sensitive to several more of our parameters than T trans,bulk.

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Future Work Synthetic data calibrations for a 1-D shock with the current code. Upgrade the code to allow modelling of ionization and electronic excitation. Couple the code with a radiation solver. Sensitivity analysis for a 1-D shock with the additional physics included. Synthetic data calibrations with the upgraded code. Calibrations with real data from EAST or similar facility.

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